Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

Deep hashing has been intensively studied and successfully applied in\nlarge-scale image retrieval systems due to its efficiency and effectiveness.\nRecent studies have recognized that the existence of adversarial examples poses\na security threat to deep hashing models, that is, adversarial vulnerability.\nNotably, it is challenging to efficiently distill reliable semantic\nrepresentatives for deep hashing to guide adversarial learning, and thereby it\nhinders the enhancement of adversarial robustness of deep hashing-based\nretrieval models. Moreover, current researches on adversarial training for deep\nhashing are hard to be formalized into a unified minimax structure. In this\npaper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the\nadversarial robustness of deep hashing models. Specifically, we conceive a\ndiscriminative mainstay features learning (DMFL) scheme to construct semantic\nrepresentatives for guiding adversarial learning in deep hashing. Particularly,\nour DMFL with the strict theoretical guarantee is adaptively optimized in a\ndiscriminative learning manner, where both discriminative and semantic\nproperties are jointly considered. Moreover, adversarial examples are\nfabricated by maximizing the Hamming distance between the hash codes of\nadversarial samples and mainstay features, the efficacy of which is validated\nin the adversarial attack trials. Further, we, for the first time, formulate\nthe formalized adversarial training of deep hashing into a unified minimax\noptimization under the guidance of the generated mainstay codes. Extensive\nexperiments on benchmark datasets show superb attack performance against the\nstate-of-the-art algorithms, meanwhile, the proposed adversarial training can\neffectively eliminate adversarial perturbations for trustworthy deep\nhashing-based retrieval. Our code is available at\nhttps://github.com/xandery-geek/SAAT.\n